aacp 1.1.0

Agent Action Compression Protocol — pipe-delimited coordination for multi-agent LLM systems

AACP — Agent Action Compression Protocol

A pipe-delimited coordination protocol for multi-agent LLM systems.

Status: v1.1-draft · Benchmarks measured · MIT Licensed · Feedback welcome IETF Internet-Draft: draft-mackay-aacp-00 Working lab: github.com/MackayAndrew/aacp-lab — 4-model payroll workflow with Excel output

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The Problem

When AI agents coordinate with each other they communicate in full natural language — verbose, ambiguous, and token-expensive. Every inter-agent instruction is an API call. In multi-agent workflows with many hops, coordination overhead compounds.

The same instruction, two ways:

English (56 tokens):
"Please retrieve the employee salary records for the period ending
31 August 2024. I need all active employees, their departments,
cost centres, base salary, any changes made this month, and pension
contribution rates. Return as JSON array."

AACP v1.1 (52 tokens):
FETCH|HR|return:HR-Agent|p:1|aacp:1.1|res:emp_salary|period:2024-08|filter:status=active|fmt:json

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Measured Results

Token counts measured from live API usage_metadata. Not estimated. 4 payroll workflow hops. Claude Sonnet 4.5 and GPT-4o. Bare coordination message only — no system prompt, no task content.

Hop	English	AACP	Claude	GPT-4o
fetch employees	56	52	-7.1%	-12.7%
fetch budgets	57	47	-17.5%	-16.0%
merge calculate	65	43	-33.8%	-31.6%
generate report	62	43	-30.6%	-33.3%
TOTAL	240	185	-22.9%	-23.7%

Consistent cross-model. All hops show reduction.

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What AACP Does

• Reduces coordination tokens by ~23% vs verbose English (measured)
• Provides structured, unambiguous, machine-parseable instructions
• Enables deterministic zero-cost encoding for known workflows
• Validates every packet against a typed schema before transmission
• Creates clean structured audit trails
• Works on Claude, GPT-4o, and GPT-4.1

What AACP Does Not Do

• Reduce task tokens — the work an agent performs is unchanged
• Guarantee total workflow cost reduction without sufficient coordination-to-task token ratio
• Replace task-level instructions — only coordination messages

Total workflow cost impact depends on your workflow type: Coordination-heavy workflows (IT provisioning, structured pipelines) benefit most. Task-heavy workflows (contract review, open-ended research) see less impact because task tokens dominate.

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Packet Format

TASK|DOM|return:AGENT|p:PRIORITY|aacp:VERSION|key:value|key:value...

TASK and DOM are positional (fields 0 and 1). All other fields are named key:value pairs. No empty positional slots.

Required: TASK DOM return: aacp: Optional: res: period: filter: fields: fmt: p:

Valid TASK values: FETCH PROC FLAG RESOLVE LOG SEND BUILD MERGE CALC REPORT ACK SYNC

Valid DOM values: HR FIN SALES LEGAL IT CS MKT

Extended fields (append as |key:value after core): src src_prev rules validate tmpl data_ptr amt ccy sup match terms type party clause issue risk block flags req highlight status to subj att tone sentiment actor chain prog ltv loyalty urgency

Examples:

# Fetch employee records
FETCH|HR|return:HR-Agent|p:1|aacp:1.1|res:emp_salary|period:2024-08|filter:status=active|fmt:json

# Merge and calculate
MERGE|HR|return:HR-Agent|p:1|aacp:1.1|rules:payroll_v2|validate:budget_cc

# Flag legal clause
FLAG|LEGAL|return:LEG-Agent|p:1|aacp:1.1|type:NDA|party:Acme-Ltd|clause:s7|issue:ip_rights_restriction|risk:high|block:signature

# IT provisioning
BUILD|IT|return:IT-Agent|p:1|aacp:1.1|res:ad_account|filter:usr=j.smith|fields:email,dept,grp,pwd_reset

# Process invoice
PROC|FIN|return:FIN-Agent|p:2|aacp:1.1|res:invoice|sup:ABC-Ltd|amt:4200|ccy:GBP|match:PO-441|terms:net30

# Resolve customer issue
RESOLVE|CS|return:CS-Agent|p:1|aacp:1.1|sentiment:negative|tone:empathetic|ltv:8000|ccy:GBP|req:goodwill_consider

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Quick Start

pip install -r requirements.txt

# Zero-cost rule-based encoding for known workflows
from aacp.encoders.workflows.payroll import PayrollEncoder

enc = PayrollEncoder()
pkt = enc.fetch_employees(period="2024-08")
print(pkt.packet)
# FETCH|HR|return:HR-Agent|p:1|aacp:1.1|res:emp_salary|period:2024-08|filter:status=active|fmt:json
print(f"Cost: ${pkt.api_cost_usd:.2f}")  # $0.00

# Validate any packet
from aacp import AACPValidator
v = AACPValidator()
print(v.validate(pkt.packet).summary())

# Decode any packet to English
from aacp import AACPDecoder
d = AACPDecoder()
print(d.decode(pkt.packet).english)

# Novel instructions — fallback to LLM, logged to registry
from aacp.encoders.fallback import FallbackEncoder
enc = FallbackEncoder()
pkt = enc.encode_english(
    english="Cross-reference new employee against background check database.",
    domain="HR",
    return_agent="HR-Agent"
)

Run the demo (no API key needed):

python3 examples/demo.py

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Workflow Encoders

Pre-built zero-cost encoders for common business workflows. Zero LLM calls. Deterministic output. $0.00 per packet.

Encoder	Workflow	Packets
PayrollEncoder	Monthly payroll run	6
ITEncoder	New employee provisioning	6
InvoiceEncoder	AP invoice processing	3
ContractEncoder	Legal contract review	3

For novel instructions: FallbackEncoder routes to LLM and logs the result to registry/unknown_patterns.json as a future rule-based candidate. One LLM call per novel pattern. Reused forever.

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Relationship to Existing Protocols

Protocol	Layer	What it does	AACP relationship
MCP (Anthropic/AAIF)	Tool access	Agent ↔ external system	AACP inside MCP payloads
A2A (Google/AAIF)	Coordination	Agent ↔ agent routing	AACP compresses A2A messages
Gibberlink/GGWave	Transport	Audio channel	Audio-specific; AACP is text-native

AACP fills the gap: semantic compression of coordination message content. No existing protocol addresses this layer.

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Prior Work

EcoLANG (Mou et al., Fudan University, May 2025, arXiv:2505.06904) independently identified agent communication verbosity as a problem and achieved >20% token reduction through evolved compression language for social simulation. AACP targets business workflow coordination with a structured packet schema rather than evolved natural language.

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Design Notes

Why pipe-delimited? Four formats were benchmarked (bracket [KEY:VALUE], JSON, pipe, abbreviated natural language). Pipe-delimited was the only format achieving consistent token reduction on both Claude and GPT-4o tokenisers. Bracket formats tokenise ~45% more expensively than English due to [, ], : character overhead.

Why keep field lists out of packets? Embedding field lists (fields:id,dept,cc,base_sal) inflates coordination tokens significantly. A well-configured persistent agent knows its default return fields. The packet communicates intent; the agent's system prompt carries the schema.

Why not URI data pointers? :// and / in URIs tokenise very inefficiently. Data references belong in agent working memory, not in coordination messages.

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Benchmarks

export ANTHROPIC_API_KEY=...
export OPENAI_API_KEY=...
python3 benchmark/tokenisation_test.py

All benchmark results published in benchmarks/. Raw JSON files available for independent verification.

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Roadmap

Version	Status	Focus
v1.0	Released	Pipe-delimited format, rule-based encoders, working SDK
v1.1	Released	Validated benchmarks — -22.9% Claude, -23.7% GPT-4o
v1.2	Planned	PyPI package (pip install aacp), TypeScript SDK
v2.0	Planned	IETF Internet-Draft, community encoding registry

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Contributing

Draft specification. Issues, PRs, and counter-proposals welcome.

aacp/           Python package — encoder, decoder, validator
benchmark/      Benchmark harness and tokenisation tests
benchmarks/     Published results (JSON + summary)
examples/       Working demos
registry/       LLM fallback pattern log

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Licence

MIT — free to use, implement, extend, and fork.

AACP is an independent protocol proposal. Not affiliated with Anthropic, Google, IBM, or the Linux Foundation, though designed to complement their respective protocol work (MCP, A2A, ACP).